scholarly journals Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mitesh S. Patel ◽  
Daniel Polsky ◽  
Dylan S. Small ◽  
Sae-Hwan Park ◽  
Chalanda N. Evans ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glycemic Control ◽  
Randomized Trial ◽  
Medical History ◽  
Laboratory Testing ◽  
Hemoglobin A1c ◽  
Activity Patterns ◽  
Ensemble Methods ◽  
Wearable Devices ◽  
Baseline Information

AbstractThe use of wearables is increasing and data from these devices could improve the prediction of changes in glycemic control. We conducted a randomized trial with adults with prediabetes who were given either a waist-worn or wrist-worn wearable to track activity patterns. We collected baseline information on demographics, medical history, and laboratory testing. We tested three models that predicted changes in hemoglobin A1c that were continuous, improved glycemic control by 5% or worsened glycemic control by 5%. Consistently in all three models, prediction improved when (a) machine learning was used vs. traditional regression, with ensemble methods performing the best; (b) baseline information with wearable data was used vs. baseline information alone; and (c) wrist-worn wearables were used vs. waist-worn wearables. These findings indicate that models can accurately identify changes in glycemic control among prediabetic adults, and this could be used to better allocate resources and target interventions to prevent progression to diabetes.

scholarly journals MON-671 Embarrassed to Eat: Two Cases of Gustatory Hyperhidrosis

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Omer H Tarar ◽  
Andres J Munoz
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Medical History ◽  
Early Identification ◽  
Hemoglobin A1c ◽  
Past Medical History ◽  
History Of ◽  
Bolus Insulin ◽  
Basal Bolus

Abstract Introduction Diabetic Gustatory Hyperhidrosis is characterized by profuse sweating with eating and may be a manifestation of Diabetic autonomic dysfunction. Most patients have evidence of other microvascular complications including nephropathy, retinopathy, peripheral neuropathy and other signs of autonomic neuropathy. We present 2 cases of gustatory hyperhidrosis associated with longstanding poorly controlled type 1 diabetes. Case 1: 49 year old Male with past medical history of longstanding type 1 diabetes with poor control, complicated with diabetic retinopathy, polyneuropathy, albuminuria presented to endocrine clinic for management of diabetes. His hemoglobin A1c was 10.8%. He was on basal-bolus Insulin at home. However, he admitted to missing most doses of prandial Insulin. On further questioning, he mentioned having episodes of profuse head and neck sweating while eating any type of food. He attributed these episodes to “low blood sugars” without checking and therefore tried to avoid Insulin. However, he continued having these episodes. He was diagnosed with Diabetic gustatory hyperhidrosis and started on topical Aluminum hexahydrate. Case 2: 34 year old Female with past medical history of long-standing DM type 1 complicated with poly- neuropathy, autonomic dysfunction, nephropathy, Retinopathy, chronic kidney disease stage III presented for follow up of her diabetes. Her hemoglobin A1c was 9.8%. She was on basal-bolus Insulin at home and reported good compliance. Given her extensive polyneuropathy, she was questioned about hyperhidrosis. She reported having profuse facial and neck sweating with eating all types of food which led to increased embarrassment while eating in public. She was diagnosed with diabetic gustatory hyperhidrosis and started on topical aluminum hexahydrate, with plans for Botox if symptoms persisted. Discussion Diabetic Gustatory Hyperhidrosis is an under- recognized condition and may be misdiagnosed as hypoglycemia, anxiety, gastroparesis or other conditions. This gustatory sweating is a source of severe distress and embarrassment for patients and can have serious emotional, social and professional implications. Associated symptoms may also be mistaken for hypoglycemia and in turn lead to nonadherence with Insulin and other diabetic medications causing suboptimal glycemic control. Topical anti-perspirants like Aluminum Chloride hexahydrate are often used as first line therapy. Second line treatment options include glycopyrrolate, Oxybutynin and Botulinum toxin. Conclusion Most patients are reluctant to mention these symptoms to health care providers and diligent history taking with specific questions in high risk patients may help in early identification and management of this condition. Early identification and management can also help promote overall confidence, quality of life and better glycemic control.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

scholarly journals Machine learning applied to simulations of collisions between rotating, differentiated planets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Miles L. Timpe ◽  
Maria Han Veiga ◽  
Mischa Knabenhans ◽  
Joachim Stadel ◽  
Stefano Marelli
Keyword(s):  
Machine Learning ◽  
Planet Formation ◽  
Critical Role ◽  
Ensemble Methods ◽  
Terrestrial Planet ◽  
Data Driven ◽  
Analytic Methods ◽  
Accurate Treatment ◽  
Post Impact ◽  
The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

Machine Learning, Ensemble Methods in

2012 ◽  
pp. 1781-1789 ◽  
Author(s):  
Sašo Džeroski ◽  
Panče Panov ◽  
Bernard Ženko
Keyword(s):  
Machine Learning ◽  
Ensemble Methods

scholarly journals Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2717
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif ◽  
Sparsh Sharma ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Digital Libraries ◽  
Stock Price ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Stock Market Prediction ◽  
Research Areas ◽  
Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

scholarly journals Replay in minds and machines

2021 ◽  
Author(s):  
Lennart Wittkuhn ◽  
Samson Chien ◽  
Sam Hall-McMaster ◽  
Nicolas W. Schuck
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Activity Patterns ◽  
Representation Learning ◽  
Parallel Machine ◽  
Artificial Agents ◽  
Active Behavior ◽  
Performance Improvements ◽  
Experience Replay ◽  
Data Efficiency

Experience-related brain activity patterns have been found to reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research have significantly expanded our understanding of the potential computational benefits replay may provide to biological and artificial agents alike. We provide an overview of findings in replay research from neuroscience and machine learning and summarize the computational benefits an agent can gain from replay that cannot be achieved through direct interactions with the world itself. These benefits include faster learning and data efficiency, less forgetting, prioritizing important experiences, as well as improved planning and generalization. In addition to the benefits of replay for improving an agent's decision-making policy, we highlight the less-well studied aspect of replay in representation learning, wherein replay could provide a mechanism to learn the structure and relevant aspects of the environment. Thus, replay might help the agent to build task-appropriate state representations.

scholarly journals Survey on Stress Detection Using Multiple Sensors through Wearable Devices

2021 ◽  
Vol 10 (2) ◽  
pp. 787-790
Keyword(s):  
Machine Learning ◽  
Wearable Devices ◽  
Anxiety And Depression ◽  
Accuracy Score ◽  
Machine Learning Method ◽  
Stress Detection ◽  
Multiple Sensors ◽  
Machine Learning Model ◽  
Wide Range ◽  
Principle Objective

An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says “Globally, more than 264 million people of all ages suffer from depression.”[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.

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